A Neurobiologically-based Neural Network Model of Risky Decision-making

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DESCRIPTION (provided by applicant): Risky decision-making leads to pervasive negative health outcomes (e.g., alcohol and drug abuse, risky sexual decisions, accidents). One central characteristic of such individuals (e.g., risky men who have sex with men (MSM)) is that they continue to engage in behaviors with very rewarding short-term consequences, but extremely negative long-term consequences, including medical, social and legal problems. Why do they have such difficulties making the right choices? A growing body of research suggests that motivated human decision-making is the result of a dynamic interplay among three systems: (1) a relatively automatic appetitive system, which has been called the "Impulsive System", (2) an executive control system, which has been called the "Reflective System"[11], and (3) a neural system that translates interoceptive signals into what one experiences as a feeling of desire, or urge [5,12] that may help propel individuals towards reward, and inhibit cognitive resources needed for self-control. Unfortunately, we lack a systematic understanding of how these complex neural systems interact with each other and with various social and contextual factors to produce risk-taking, when, for whom, and why. This gap impedes more rapid advancements in prevention and intervention science. Adequate computational tools could help address this critical barrier, and better advance a cumulative science, but they are currently lacking. This project aims to address this gap by developing generalizable computational tools: A validated neurobiologically based, neural network model of the interaction of these systems could transform our ability to advance theory and effective interventions. To this end, a team of social scientists, neuroscientists, and computational neuroscientists will (a) develop biologically-based computational models that leverage and integrate existing neural network models that view behavior as emergent from approach and avoid motivational structures[13,14] and, at a different level of scale, neural network models that simulate the underlying biological basis of incentive processing and learning, executive function, and decision-making [15,16]; (b) test, validate, and refine the model by predicting the neural and behavioral responses of a subsample from 180 young MSM (sexually risky, sexually risky methamphetamine users, and non-risky) from a completed NIDA imaging study on risky decision-making; (c) assess its generalizability via focused tests, and cross validate with additional NIDA data subsamples; and (d) conduct exploratory computational analyses aimed at concurrently predicting MSM's sequential neural and behavioral dynamics in a virtual date simulation over time, and using the model to explore what interventions, when, and for whom might more effectively reduce risk-taking. A deeper understanding of these neural systems and their interactions, will transform our ability to advance theory, design effective risk-reduction interventions and enhance societal health and well-being, while reducing economic costs.